Rainfall-runoff models are used for efficient management, distribution, planning, and design of water
resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff
as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models
simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate
simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are
collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation
results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this
paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation
outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval
from probabilistic distribution of a model’s error, can quantify global uncertainty of hydrological models. In this
paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from VfloTM, a
physically-based distribution model and HEC-HMS model, a conceptual lumped model.